...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Mathematical Modelling >An integrative model for the identification of key players of cancer networks
【24h】

An integrative model for the identification of key players of cancer networks

机译：识别癌症网络关键参与者的综合模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Uncovering miscoordination in a biological network is essential for the understanding of cellular malfunctions in cancer. Integrative analysis across multiple cellular levels may provide an opportunity to elucidate the miscoordination between the regulatory mechanisms in cancer cells.Here, we propose an integrative model for the identification of key players of the cancer-activated Multi-Type Interaction (MTI) gene network (KPOCN). To measure the functional associations between genes, using DNA copy number aberrations (CNAs) and gene expressions (GEs), we constructed three interacting weighted graphs: GEs affected by CNAs, CNAs by CNAs, and GEs by GEs. These three weighted graphs were mapped onto a single graph, in order to construct a MTI gene network by using their optimal combination. Finally, the effect of a single gene was determined by using the centrality and betweenness of node scores in the MTI network.We first tested KPOCN using simulated datasets, and afterward, we applied this model to the real breast cancer datasets. KPOCN was shown to identify successfully key regulators with their corresponding response variables (targets) when using the simulated data, and identified well-known breast cancer oncogenes. These results demonstrated that our model can be used for an efficient identification of key genes that affect cancer development. Source codes are available at http://gcancer.org/KPOCN.
机译：在生物网络中发现配位失调对于理解癌症中的细胞功能异常至关重要。跨多个细胞水平的整合分析可能为阐明癌细胞调控机制之间的不协调提供机会。在此，我们提出了一个整合模型，用于鉴定癌症激活的多型相互作用（MTI）基因网络的关键参与者（ KPOCN）。为了测量基因之间的功能关联，使用DNA拷贝数畸变（CNA）和基因表达（GEs），我们构建了三个相互作用的加权图：受CNA影响的GE，受CNA影响的CNA和受GEs影响的GE。将这三个加权图映射到单个图上，以便通过使用它们的最佳组合来构建MTI基因网络。最后，通过使用MTI网络中节点评分的中心性和中间性来确定单个基因的效果。我们首先使用模拟数据集对KPOCN进行了测试，然后将该模型应用于实际的乳腺癌数据集。当使用模拟数据时，KPOCN被证明可以成功识别关键调节剂及其相应的反应变量（靶标），并鉴定出著名的乳腺癌癌基因。这些结果表明，我们的模型可用于有效鉴定影响癌症发展的关键基因。源代码可从http://gcancer.org/KPOCN获得。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号